1. Motion Picture Revenue Prediction An Artificial Neural Network Method for Predicting Opening Weekend Box-Office Revenue ECE 539 – Fall 2001 Final Project Chad M. Steighner ID: 253-699-5562

2. Concept  Use opening weekend revenues from 1989 through 2000 to train a MLP with back-propagation for classification into 5 classes.  Input features include: Genre, MPAA rating, Date, # Screens, Critical Rating, Distributor, Run-time and Weekend Length  The MLP is then tested with motion picture data from 2001.

3. Implementation  Found 473 films with opening weekend data (www.boxofficeguru.com)www.boxofficeguru.com  Used www.imdb.com to obtain add’l fieldswww.imdb.com  Created Parsedata.java to construct TrainingData and TestingData for MLP 432 Training (1989 – 2000) 41 Testing (2001)  Through testing and 3-way cross validation found the best set-up to be: 3 Layer MLP (1 hidden) 6 hidden neurons Learning rate = 0.1 Momentum = 0.9 1000 Epochs

4. Results  45.6% avg. class. of 2001 films. (5.87 St.dev) $10-$12M, $12-$14M, $14-$17M, $17-$28M, $28M+  No Exact Replica Baseline Study: Nat’l Research Group (LA) - telephone surveys to within 5% of opening weekend revenue. Moviefone claims to be even closer (movie info website) Prof. Arthur De Vany (UC-Irvine) Bose-Einstein distribution of particles falling into urns. Equally likely particles (audience) will fall into a few urns (movies) as it is for them to be distributed in any other way.